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. 2021 Jan 5;22:14. doi: 10.1186/s13059-020-02251-5

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© The Author(s) 2021

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — Codon optimality is the major determinant of mRNA stability. a Diagram depicting the maternal to zygotic transition in zebrafish. b Scatter plots of predicted and observed mRNA stability, during MZT, for maternal mRNAs in zebrafish and Xenopus. The gradient of color represents the content of optimal codons [22]. The predicted mRNA stability correlates with the proportion of optimal codons (p < 2 × 10⁻¹⁶, Pearson correlation). The mRNA stability median of mRNAs enriched in optimal (red) or non-optimal (blue) codons, as well as mRNAs with miR-430/-427 target sites in the 3′UTR (green) are shown. c Codon content explains most of the mRNA decay during MZT. The x-axis shows the fraction of coding genes that can be regulated by different mRNA stability pathways. For microRNAs, this fraction corresponds to the number of seed sites (GCACTT) in the 3′UTR, and for m6A, the fraction is the number of target genes reported [10]. The y-axis shows the Bayesian model comparison weights [32]. These weights represent which model is more likely to predict unobserved data better, higher values indicate stronger regulatory effects